On Automatic Modeling and Use of Domain-specific Ontologies

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskning

    Resumé

    In this paper, we firstly introduce an approach to the modeling of a domain-specific ontology for use in connection with a given document collection. Secondly, we present a methodology for deriving conceptual similarity from the domain-specific ontology. Adopted for ontology representation is a specific lattice-based concept algebraic language by which ontologies are inherently generative. The modeling of a domain specific ontology is based on a general ontology built upon common knowledge resources as dictionaries and thesauri. Based on analysis of concept occurrences in the object document collection the general ontology is restricted to a domain specific ontology encompassing concepts instantiated in the collection. The resulting domain specific ontology and similarity can be applied for surveying the collection through key concepts and conceptual relations and provides a means for topic-based navigation. Finally, a measure of concept similarity is derived from the domain specific ontology based on occurrences, commonalities, and distances in the ontology.
    OriginalsprogEngelsk
    TitelLecture Notes in Artificial Intelligence
    ForlagSpringer
    Publikationsdato2005
    StatusUdgivet - 2005
    Begivenhed15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005 - Saratoga Springs, USA
    Varighed: 25 maj 200528 maj 2005
    Konferencens nummer: 15

    Konference

    Konference15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005
    Nummer15
    LandUSA
    BySaratoga Springs
    Periode25/05/200528/05/2005

    Citer dette

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    title = "On Automatic Modeling and Use of Domain-specific Ontologies",
    abstract = "In this paper, we firstly introduce an approach to the modeling of a domain-specific ontology for use in connection with a given document collection. Secondly, we present a methodology for deriving conceptual similarity from the domain-specific ontology. Adopted for ontology representation is a specific lattice-based concept algebraic language by which ontologies are inherently generative. The modeling of a domain specific ontology is based on a general ontology built upon common knowledge resources as dictionaries and thesauri. Based on analysis of concept occurrences in the object document collection the general ontology is restricted to a domain specific ontology encompassing concepts instantiated in the collection. The resulting domain specific ontology and similarity can be applied for surveying the collection through key concepts and conceptual relations and provides a means for topic-based navigation. Finally, a measure of concept similarity is derived from the domain specific ontology based on occurrences, commonalities, and distances in the ontology.",
    author = "Troels Andreasen and Rasmus Knappe and Henrik Bulskov",
    year = "2005",
    language = "English",
    booktitle = "Lecture Notes in Artificial Intelligence",
    publisher = "Springer",

    }

    Andreasen, T, Knappe, R & Bulskov, H 2005, On Automatic Modeling and Use of Domain-specific Ontologies. i Lecture Notes in Artificial Intelligence. Springer, 15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005, Saratoga Springs, USA, 25/05/2005.

    On Automatic Modeling and Use of Domain-specific Ontologies. / Andreasen, Troels; Knappe, Rasmus; Bulskov, Henrik.

    Lecture Notes in Artificial Intelligence. Springer, 2005.

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskning

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    AU - Knappe, Rasmus

    AU - Bulskov, Henrik

    PY - 2005

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    N2 - In this paper, we firstly introduce an approach to the modeling of a domain-specific ontology for use in connection with a given document collection. Secondly, we present a methodology for deriving conceptual similarity from the domain-specific ontology. Adopted for ontology representation is a specific lattice-based concept algebraic language by which ontologies are inherently generative. The modeling of a domain specific ontology is based on a general ontology built upon common knowledge resources as dictionaries and thesauri. Based on analysis of concept occurrences in the object document collection the general ontology is restricted to a domain specific ontology encompassing concepts instantiated in the collection. The resulting domain specific ontology and similarity can be applied for surveying the collection through key concepts and conceptual relations and provides a means for topic-based navigation. Finally, a measure of concept similarity is derived from the domain specific ontology based on occurrences, commonalities, and distances in the ontology.

    AB - In this paper, we firstly introduce an approach to the modeling of a domain-specific ontology for use in connection with a given document collection. Secondly, we present a methodology for deriving conceptual similarity from the domain-specific ontology. Adopted for ontology representation is a specific lattice-based concept algebraic language by which ontologies are inherently generative. The modeling of a domain specific ontology is based on a general ontology built upon common knowledge resources as dictionaries and thesauri. Based on analysis of concept occurrences in the object document collection the general ontology is restricted to a domain specific ontology encompassing concepts instantiated in the collection. The resulting domain specific ontology and similarity can be applied for surveying the collection through key concepts and conceptual relations and provides a means for topic-based navigation. Finally, a measure of concept similarity is derived from the domain specific ontology based on occurrences, commonalities, and distances in the ontology.

    M3 - Article in proceedings

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    PB - Springer

    ER -

    Andreasen T, Knappe R, Bulskov H. On Automatic Modeling and Use of Domain-specific Ontologies. I Lecture Notes in Artificial Intelligence. Springer. 2005